TITLE: The measure-theoretical definition of a bootstrap sample
QUESTION [6 upvotes]: I’m currently learning the bootstrap method, and I have two questions to ask about the definition of a bootstrap sample.

Let $ (\Omega,\mathscr{S},\mathsf{P}) $ be a probability space. Let $ X_{1},\ldots,X_{n} $ be i.i.d. random variables on $ (\Omega,\mathscr{S},\mathsf{P}) $, with their common c.d.f. denoted by $ F $. Let $ \hat{F} $ denote the empirical c.d.f. of $ X_{1},\ldots,X_{n} $, i.e.,
$$
\forall x \in \mathbf{R}: \qquad
\hat{F}(x) = \frac{1}{n} \sum_{i = 1}^{n} \chi_{(- \infty,x]} \circ X_{i}.
$$
Clearly, $ \hat{F}(x) $ is a random variable on $ (\Omega,\mathscr{S},\mathsf{P}) $ for each $ x \in \mathbf{R} $, and for each $ \omega \in \Omega $, the function
$$
\left\{ \begin{matrix}
\mathbf{R} & \to & [0,1] \\ x & \mapsto & \left[ \hat{F}(x) \right] \! (\omega)\end{matrix} \right\}
$$
is the c.d.f. of some discrete random variable.

Question 1: What does it mean to say that $ (X_{1}^{*},\ldots,X_{n}^{*}) $ is a bootstrap sample drawn from $ \hat{F} $? As mentioned, $ \hat{F}(x) $ is not a number but a random variable for each $ x \in \mathbf{R} $. I require an answer to this question strictly in terms of measure theory.
Question 2: What probability space are $ X_{1}^{*},\ldots,X_{n}^{*} $ defined on? Is it still $ (\Omega,\mathscr{S},\mathsf{P}) $?

Thanks!

REPLY [2 votes]: Here is a complementary (but equivalent) construction of the bootstrap sample:
Let $(\Omega_1,\Sigma_1,P_1)$ be a probability space and consider the $n$-fold product space $(\Omega^S,\Sigma^S,P^S)$ with coordinate maps $X_1,\ldots,X_n$. In other words, $X_1,\ldots,X_n$ is the canonical choice of of i.i.d. random variables with law $P_1$, and $(\Omega^S,\Sigma^S,P^S)$ is the space of our original sample.
Next, consider the probability space $(\Omega_2,\Sigma_2,P_2)$, where $\Omega_2=\{1,\ldots,n\}$ is the $n$-point set, $\Sigma_2$ is the power set $\sigma$-algebra and $P_2$ is the uniform measure. Let $(\Omega^B,\Sigma^B,P^B)$ denote the $n$-fold product of this space with coordinate maps $\tau_1,\ldots,\tau_n$. Thus, $\tau_1,\ldots,\tau_n$ are i.i.d. uniformly distributed on $\{1,\ldots,n\}$.
Finally, let $(\Omega,\Sigma,P)$ denote the product of $(\Omega_1,\Sigma_1,P_1)$ and $(\Omega_2,\Sigma_2,P_2)$. The variables $X_1,\ldots,X_n$ can be viewed as variables in $(\Omega,\Sigma,P)$ by putting $X_j(\omega):=X_j(\omega^S)$ for $\omega=(\omega^S,\omega^B)$, and similarly for $\tau_1,\ldots,\tau_n$.

Definition: The bootstrap sample $X^*_1,\ldots,X^*_n$ of $X_1,\ldots,X_n$ is defined by $X^*_j:=X_{\tau_j}$.

That is, $X^*_j(\omega)=X_{\tau_j(\omega)}(\omega)=X_{\tau_j(\omega^B)}(\omega^S)$, or equivalently $X^*_j=\sum_{k=1}^n X_k1_{(\tau_j=k)}$.
Since $P(\tau_j=k)=1/n$ and $X_1,\ldots,X_n,\tau_1,\ldots,\tau_n$ are independent, we see that almost surely
$$
E(1_A(X^*_j)\mid X_1,\ldots,X_n)=\frac{1}{n}\sum_{k=1}^n E(1_A(X_k)\mid X_1,\ldots,X_n) = \frac{1}{n}\sum_{k=1}^n 1_A(X_k) = \frac{1}{n}\sum_{k=1}^n \delta_{X_k}(A).
$$
This ensures, as expected, that the empirical measure $P_n(A,\omega):=\frac{1}{n}\sum_{j=1}^n \delta_{X_j(\omega)}(A)$ is a conditional distribution for $X_j^*$ given $X_1,\ldots,X_n$. Similarly, we have almost surely
\begin{align}
&E(1_{A_1}(X^*_1)\cdots 1_{A_n}(X^*_n)\mid X_1,\ldots,X_n) \\
&=\frac{1}{n^n}\sum_{k_1=1}^n\cdots \sum_{k_n=1}^n 1_{A_1}(X_{k_1})\cdots 1_{A_n}(X_{k_n})\\
&=E(1_{A_1}(X^*_1)\mid X_1,\ldots,X_n)\cdots E(1_{A_n}(X^*_n)\mid X_1,\ldots,X_n),
\end{align}
which ensures that $X_1^*,\ldots,X_n^*$ are conditionally independent given $X_1,\ldots,X_n$.
Dudley, R.M., Real analysis and probability., Cambridge Studies in Advanced Mathematics. 74. Cambridge: Cambridge University Press. x, 555 p. (2002). ZBL1023.60001.
Dudley, R. M., Uniform central limit theorems, Cambridge Studies in Advanced Mathematics 142. Cambridge: Cambridge University Press (ISBN 978-0-521-73841-5/pbk; 978-0-521-49884-5/hbk; 978-1-139-01483-0/ebook). 482 p. (2014). ZBL1317.60030.